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Found 6 papers, 2 papers with code
C3Net: Compound Conditioned ControlNet for Multimodal Content Generation
Specifically, C3Net first aligns the conditions from multi-modalities to the same semantic latent space using modality-specific encoders based on contrastive training.
Dependency Relationships-Enhanced Attentive Group Recommendation in HINs
Finally, we develop an attention aggregator that aggregates users' preferences as the group's preferences for the group recommendation task.
CoDeF: Content Deformation Fields for Temporally Consistent Video Processing
We present the content deformation field CoDeF as a new type of video representation, which consists of a canonical content field aggregating the static contents in the entire video and a temporal deformation field recording the transformations from the canonical image (i. e., rendered from the canonical content field) to each individual frame along the time axis. Given a target video, these two fields are jointly optimized to reconstruct it through a carefully tailored rendering pipeline. We advisedly introduce some regularizations into the optimization process, urging the canonical content field to inherit semantics (e. g., the object shape) from the video. With such a design, CoDeF naturally supports lifting image algorithms for video processing, in the sense that one can apply an image algorithm to the canonical image and effortlessly propagate the outcomes to the entire video with the aid of the temporal deformation field. We experimentally show that CoDeF is able to lift image-to-image translation to video-to-video translation and lift keypoint detection to keypoint tracking without any training. More importantly, thanks to our lifting strategy that deploys the algorithms on only one image, we achieve superior cross-frame consistency in processed videos compared to existing video-to-video translation approaches, and even manage to track non-rigid objects like water and smog. Project page can be found at https://qiuyu96. github. io/CoDeF/.
Elastically-Constrained Meta-Learner for Federated Learning
One of the challenges in federated learning is non-IID data between clients, as a single model can not fit the data distribution for all clients.
Dynamic Multi-Scale Loss Optimization for Object Detection
With the continuous improvement of the performance of object detectors via advanced model architectures, imbalance problems in the training process have received more attention.
CE-FPN: Enhancing Channel Information for Object Detection
Instead of the original 1x1 convolution and linear upsampling, it mitigates the information loss due to channel reduction.
